Efficient Trustworthiness Management for Malicious User Detection in Big Data Collection

Yu, Jiahui; Wang, Kun; Xia, Rui; Guo, Song; Guo, Mei

doi:10.1109/tbdata.2017.2761386

Cited by 20 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous indicators may be used to measure the success of a system built to identify harmful user behaviour using CLM and optimization techniques [28] [31]. Considering the frequently used assessment metrics.…”

Section: B Experimental Resultsmentioning

confidence: 99%

A Sophisticated Deep Learning Framework of Advanced Techniques to Detect Malicious Users in Online Social Networks

Terumalasetti,

2023

IJACSA

View full text Add to dashboard Cite

Malicious user detection is a cybersecurity exploration domain because of the emergent jeopardies of data breaches and cyberattacks. Malicious users have the potential to detriment the system by engaging in unauthorized actions or thieving sensitive data. This paper proposes the dual-powered CLM technique (Convolution neural networks and LSTM) and optimization technique, a sophisticated methodology for distinguishing malicious user behavior that assimilates LSTM and CNN, and finally optimization technique to enhance the results. A genetic algorithm is used to augment the model's capability to perceive altering and nuanced malicious performance by fine-tuning its parameters. Due to the rising vulnerabilities of data breaches and cyber-attacks, malicious user identification in OSN (Online Social Networks) is a significant topic of research in cybersecurity. The proposed technique pursues to ascertain anomalous user behavior patterns by assessing vast quantities of data generated by digital systems with CLM and optimizing detection accuracy with genetic algorithms. On a public dataset of social media bot dataset, a twibot-20 dataset comprehending user activity data, was explored to measure the performance of the suggested methodology. The outcomes demonstrated that, in comparison to conventional machine learning algorithms like SVM and RF, which respectively obtained 92.3% and 88.9% accuracy, our technique, had a better accuracy of 98.7%. Moreover, the other metrics measures were assessed, and the proposed technique outperformed traditional machine learning algorithms in each situation.

show abstract

Section: B Experimental Resultsmentioning

confidence: 99%

A Sophisticated Deep Learning Framework of Advanced Techniques to Detect Malicious Users in Online Social Networks

Terumalasetti,

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…To identify anomalous and bot accounts, many previous studies have leverage user-generated data and deploy machine learning methods in mining the differences between normal and malicious users. Typically, user-generated data contains diverse user activities and user interests during using an app or mobile devices, such as their interactions with items, their mobility patterns, their followings of other user accounts and some other temporal records [58,140,145]. Especially when considering the user data generated from mobile phone, we can build feature-rich demographic information for a user because the data collected from mobile phone contains much more useful information than an single app.…”

Section: Identify User Professions Based On Privacy-preserved Mobile ...mentioning

confidence: 99%

User identification and community exploration via mining big personal data in online platforms

Zhang¹

View full text Add to dashboard Cite

User-generated big data mining is vital important for large online platforms in terms of security, profits improvement, products recommendation and system management. Personal attributes recognition, user behavior prediction, user identification, and community detection are the most critical and interesting issues that remain as challenges in many real applications in terms of accuracy, efficiency and data security. For an online platform with tens of thousands of users, it is always vulnerable to malicious users who pose a threat to other innocent users and consume unnecessary resources, where accurate user identification is urgently required to prevent corresponding malicious attempts. Meanwhile, accurate prediction of user behavior will help large platforms provide satisfactory recommendations to users and efficiently allocate different amounts of resources to different users. In addition to individual identification, community exploration of large social networks that formed by online databases could also help managers gain knowledge of how a community evolves. And such large scale and diverse social networks can be used to validate network theories, which are previously developed from synthetic networks or small real networks. In this thesis, we study several specific cases to address some key challenges that remain in different types of large online platforms, such as user behavior prediction for cold-start users, privacy protection for user-generated data, and large scale and diverse social community analysis.

show abstract

Mining Network Security Holes Based on Data Flow Analysis in Smart Grid

Li¹,

Liu

Zhang

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Efficient Trustworthiness Management for Malicious User Detection in Big Data Collection

Cited by 20 publications

References 46 publications

A Sophisticated Deep Learning Framework of Advanced Techniques to Detect Malicious Users in Online Social Networks

A Sophisticated Deep Learning Framework of Advanced Techniques to Detect Malicious Users in Online Social Networks

User identification and community exploration via mining big personal data in online platforms

Mining Network Security Holes Based on Data Flow Analysis in Smart Grid

Contact Info

Product

Resources

About